Lithogeochemical classification of igneous rocks using Streckeisen ternary diagrams
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Notice bibliographique
Résumé
Mineral deposit models strategically guide exploration. The lithologies from which these models are built have genetic connotations. Thus, rock classification must be accurate to ensure that mineral exploration is effective and successful. Rock classification is based on mineral proportions, and these are commonly determined by: (1) visual inspection, which is subject to large errors; (2) point counting, which is tedious and time-consuming; (3) image analysis of stained slabs or polished thin sections, which is expensive and constrained by the availability of appropriate stains; and (4) image analysis of spectrometric data, which is expensive. These features make rock classification difficult and undermine its quality, thereby negatively impacting geological conclusions and mineral exploration results. A novel alternative procedure for igneous rock classification involves using whole rock lithogeochemical data for classification on Streckeisen ternary diagrams. This approach employs several calculations that transform: (1) mass-based element concentrations (the original lithogeochemical data produced by the laboratory) sequentially into (2) unstandardized (do not sum to unity) molar element numbers; (3) unstandardized molar mineral numbers; (4) unstandardized volume mineral numbers; and finally (5) standardized (closed; sum to unity) volume mineral concentrations that estimate the mineral modes in rocks. These mineral mode estimates can then be plotted on (projected onto) Streckeisen ternary diagrams, to classify the rocks in the normal manner. This new approach has advantages over conventional classification strategies, in that it is relatively inexpensive, adaptable to all forms of igneous rocks, quantitative, accurate, and precise. Required petrographic information necessary to conduct such a classification includes only knowledge of chemical formulae of the ‘essential’ mineral assemblage. Essential minerals are, here, considered those minerals having concentrations exceeding 5% in 5% of the rocks under consideration. This criterion allows this lithogeochemical classification procedure to be applicable to a wide variety of igneous rocks. This lithogeochemical classification procedure has additional applications beyond the classification of plutonic igneous rocks. For example, if an essential mineral assemblage can be identified or hypothesized, classification of felsic or mafic volcanic rocks can also be achieved. Additionally, an essential mineral assemblage does not have to consist exclusively of igneous minerals. As a result, conversion from molar element numbers to molar mineral numbers can be undertaken using many mineral assemblages. This allows analogous lithogeochemical classification to be undertaken for almost any rock type (e.g. clastic sedimentary rocks, using the calculated proportions of quartz, feldspar, and clay minerals). Consequently, lithogeochemical calculation of the essential mineral modes in rocks can be used to establish mineral zoning maps in space or time, allowing exploration geoscientists to create down-hole logs depicting hydrothermal alteration mineral abundances, or surface maps of hydrothermal alteration zones on a mineral property. To demonstrate this new procedure, results from classifications of metaluminous, peraluminous, and alkaline felsic plutonic and volcanic rocks, and mafic and ultramafic plutonic and volcanic rocks are compared with mineral modes acquired by independent means (visual estimates, point counts, image analysis, spectrometry). These case studies demonstrate that the proposed lithogeochemical classification procedure is as or more accurate than conventional classification methods. Furthermore, because lithogeochemical samples are far larger, and thus more representative than the surfaces used to estimate mineral modes by conventional means, this lithogeochemical classification procedure is also far more precise. The resulting classification is thus especially effective when working with fine-grained rocks where mineral identification and volume estimation is difficult.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle